About me

I am a Data Visualization, Machine Learning, and AI enthusiast with a passion for solving real-world problems through data-driven solutions. I have experience in developing and implementing ML models for various business applications.

My skills include programming languages like Python, R, and experience with various libraries such as Tensorflow, Keras, and scikit-learn. I am continually seeking to expand my knowledge and skills through business projects and AI/ML collaboration. My portfolio showcases my ability to turn data into actionable insights and implement cutting-edge ML/AI solutions. Let's work together to make an impact with ML/AI!

What I Can Deliver

  • data icon

    Data Visualization

    Unlock the insights hidden within your data with visually appealing informative data visualization services.

  • AI/ML icon

    AI/ML

    Transform your business with a cutting-edge AI and ML service, harnessing the power of data to drive innovation and growth.

Daniel lewis
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Daniel lewis

Richard was hired to create a corporate identity. We were very pleased with the work done. She has a lot of experience and is very concerned about the needs of client. Lorem ipsum dolor sit amet, ullamcous cididt consectetur adipiscing elit, seds do et eiusmod tempor incididunt ut laborels dolore magnarels alia.

Resume

Education

  1. ASU W.P. Carey School of Business

    2021 — 2023

    Masters in Business Administration (MBA)

  2. Rice University

    2015 — 2019

    Bachelor of Science in Bioengineering (B.S. Bioengineering)

Experience

  1. Data Visualization

    2021 — Present

    • Performed a temporal analysis on an eBay auction data set to analyze the relationship between the number of sniper bids and auction closing price using Tableau.

    • Executed a deviation analysis on a Housing Affordability dataset using Tableau to visualize the difference between median household income and minimum income requirement across various cities in the United States.

    • Conducted a distribution analysis on an Olympics data set to visualize the distribution of Olympics medals by country using Tableau.

    • Performed a geospatial analysis on a NBA dataset using tableau to visualize shooting percentage from different areas of the court by NBA player.

    • Created a tree map to visualize all stocks in the S&P 500 based on market cap, sector, and industry using Tableau.

    • Performed a network analysis on a Chicago Transportation Authority (CTA) system map to visualize through a dashboard all train lines within the CTA rail ‘L’ and which stations are ADA accessible.

    • Designed a Tableau dashboard to visualize on a map departure performance from PHX Sky Harbor by airline and destination.

    • Analyzed Napa Winery Sales data set focusing on trends in customer purchases, revenues by customer segment/region, and revenues by sales channel using Tableau. Key findings compiled into a Tableau Story board.

  2. AI/ML

    2021 — Present

    • Performed an exploratory analysis of a San Francisco Salary dataset to characterize total pay by job title using Python pandas and seaborn libraries.

    • Executed an unsupervised learning k-means cluster analysis on a pharmaceutical company dataset to create a basket of pharmaceutical companies with similar stock attributes using Python pandas and sklearn libraries for preprocessing. Leveraged elbow method to identify optimal k value. Created parallel coordinates visualization using matplotlib and Pandas libraries to visualize cluster attributes. Graphed clusters on a scatterplot using seaborn library.

    • Performed a hierarchical cluster analysis on an automobile dataset to group various car models together based on model attributes. Performed data preprocessing using Python pandas and sklearn libraries. Created dendrogram using SciPy, matplotlib, and NumPy libraries.

    • Performed association rule analysis on a grocery basket data set with the goal of identify grocery items that are typically bought together and thus, associated with each other.

    • Created a supervised learning predictive model on insurance cost data using Python. Leveraged seaborn and matplotlib libraries to create a correlation heat map between features and the desired target variable (insurance cost). Leveraged sklearn library to split data into training and test set. Developed an OLS regression model using stats models API. Evaluated model using sklearn and NumPy libraries.

    • Developed a neural network predictive model for a laptop sales data set with the aim of predicting retail price. Leveraged Keras library to define neural model attributes. Evaluated model using sklearn library.

    • Leveraged Azure ML to perform data mining on a credit card approval dataset. Dataset was used to develop a binary classification model with the target variable being an accept/reject decision based on applicant attributes. Compared the results of a two-class boosted decision tree, two-class logistic regression, and two-class neural network model utilizing confusion matrices.

    • Leveraged Azure ML to perform a sentiment analysis on a restaurant reviews dataset with the goal of building a binary classification model that suggests a positive/negative review based on data features present.

My skills

  • Tableau
    90%
  • Python
    90%
  • ML Azure
    80%
  • SQL
    70%